Text Classification
Transformers
ONNX
Safetensors
English
modernbert
rag
governance
hallucination-detection
evidence-verification
pyrrho
fitz-gov-v2
multi-label-classification
text-embeddings-inference
Instructions to use yafitzdev/pyrrho-v2-nano-g1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yafitzdev/pyrrho-v2-nano-g1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yafitzdev/pyrrho-v2-nano-g1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yafitzdev/pyrrho-v2-nano-g1") model = AutoModelForSequenceClassification.from_pretrained("yafitzdev/pyrrho-v2-nano-g1") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -24,7 +24,9 @@ It replaces the older Pyrrho g5 multitask shape with four v2 heads:
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The model is intended for local governance in `fitz-sage`: decide whether
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retrieved evidence is sufficient, insufficient, or disputed, and expose
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actionable retrieval/failure metadata.
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## Inputs
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[2] <retrieved source text>
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```text
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Question: <user query>
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```
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## Output Decoding
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The model is intended for local governance in `fitz-sage`: decide whether
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retrieved evidence is sufficient, insufficient, or disputed, and expose
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actionable retrieval/failure metadata. In `fitz-sage`, v2 g1 is used for
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post-retrieval evidence governance. Query-only pre-retrieval planning remains
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disabled until a query-trained v2 head is available.
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## Inputs
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[2] <retrieved source text>
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```
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The raw model can be run on query-only text, but `fitz-sage` does not use those
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logits for first-pass retrieval planning in this release.
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## Output Decoding
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